Hardware-Enabled Mechanisms for Verifying Responsible AI ... - arXiv

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arXiv:2505.03742v1 \[cs.CR\] 02 Apr 2025

# Hardware-Enabled Mechanisms for Verifying Responsible AI Development

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Aidan O’Gara
Equal contribution
Gabriel Kulp\*Gabriel Kulp is currently serving as a Technology and Security Policy Fellow at RAND; however, the views, opinions, findings, conclusions, and recommendations contained herein are the author’s alone and not those of RAND or its research sponsors, clients, or grantors.
Will Hodgkins\*James Petrie
Vincent Immler
Aydin Aysu
Kanad Basu
Shivam Bhasin
Stjepan Picek
Ankur Srivastava

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Abstract

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Advancements in AI capabilities, driven in large part by scaling up computing resources used for AI training, have created opportunities to address major global challenges but also pose risks of misuse. Hardware-enabled mechanisms (HEMs) can support responsible AI development by enabling verifiable reporting of key properties of AI training activities such as quantity of compute used, training cluster configuration or location, as well as policy enforcement. Such tools can promote transparency and improve security, while addressing privacy and intellectual property concerns. Based on insights from an interdisciplinary workshop, we identify open questions regarding potential implementation approaches, emphasizing the need for further research to ensure robust, scalable solutions.

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## Executive summary

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Recent years have seen dramatic progress in AI systems’ capabilities, in large part achieved through scaling the amount of computation used in the training process. Displaying highly general capabilities, the most sophisticated AI models have the potential to generate significant economic value and help to solve pressing problems facing humanity, but could also facilitate various forms of malicious or harmful use. The potential role of AI hardware in promoting responsible AI development has recently attracted increasing interest from policy-makers and researchers
(Kulp et al., [2024](https://arxiv.org/html/2505.03742v1#bib.bib34 ""), The Bipartisan Senate AI Working Group, [2024](https://arxiv.org/html/2505.03742v1#bib.bib72 "")).

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This report outlines various hardware-enabled mechanisms that aim to advance certain AI policy goals based on functionality embedded securely into the hardware used to develop and run AI systems. We focus particularly on two classes of mechanisms: mechanisms that can enhance the visibility of governments, external auditors or other relevant stakeholders regarding how AI models are being developed, and mechanisms that can enable enforcement of regulations or agreements relating to high-risk AI development activities. For each mechanism, we discuss the current state of understanding re

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